An Intelligent Grading System for Automated Identification and Classification of Banana Fruit Diseases Using Deep Neural Network

Abstract

This study proposes an intelligent system for automated diseases detect and categorization in banana fruit, as well as an integrated grading system. To accomplish accurate illness identification and grading, the suggested system combines computer vision methods, machine learning algorithms, and deep learning models. The system extracts key information from banana fruit images using image processing techniques, which are subsequently input into a trained classification model. The categorization model uses cutting-edge algorithms to categorize the banana fruit into several illness groups. Furthermore, the sophisticated grading system evaluates the severity and quality of the diseased fruit based on a variety of characteristics such as size, color, and texture. The experimental findings reveal that the proposed method is successful, with high accuracy rate of 99.8% in illness diagnosis and accurate banana grading. This automated technology provides a time-efficient and cost-effective approach for disease control in banana plantations, allowing producers and agricultural stakeholders to make more informed decisions.